web_demo/app.py

import os
import cv2
import torch
import numpy as np
import time
from datetime import datetime
import threading
import base64
from werkzeug.utils import secure_filename

from flask import Flask, render_template, Response, request, jsonify
from flask_socketio import SocketIO

# Important: Make sure your custom utility scripts are accessible
from utils.load_model import load_models
from utils.utils import build_transforms
from network.TorchUtils import get_torch_device
from yolo_detection import analyze_video_with_yolo

# ---- App Setup ----
app = Flask(__name__)
app.config['SECRET_KEY'] = 'your_secret_key!'

# ADDED: Configuration for uploaded files
UPLOAD_FOLDER = 'uploads'
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER

socketio = SocketIO(app, async_mode='eventlet')

# ---- Global Config & Model Loading ----
print("[INFO] Loading models...")
DEVICE = get_torch_device()
FEATURE_EXTRACTOR_PATH = r"S:\\ano_dec_pro\\AnomalyDetectionCVPR2018-Pytorch\\pretrained\\c3d.pickle"
AD_MODEL_PATH = r"S:\\ano_dec_pro\\AnomalyDetectionCVPR2018-Pytorch\\exps\\c3d\\models\\epoch_80000.pt"
YOLO_MODEL_PATH = r"S:\\ano_dec_pro\\AnomalyDetectionCVPR2018-Pytorch\\yolo_my_model.pt"
SAVE_DIR = "outputs/anomaly_frames"
os.makedirs(SAVE_DIR, exist_ok=True)

anomaly_detector, feature_extractor = load_models(
    FEATURE_EXTRACTOR_PATH, AD_MODEL_PATH, features_method="c3d", device=DEVICE
)
feature_extractor.eval()
anomaly_detector.eval()
TRANSFORMS = build_transforms(mode="c3d")
ANOMALY_THRESHOLD = 0.4
print("[INFO] Models loaded successfully.")

VIDEO_PATHS = {
    "Abuse": r"S:\\ano_dec_pro\AnomalyDetectionCVPR2018-Pytorch\web_demo\static\\videos\\Abuse.mp4",
    "Arrest": r"S:\\ano_dec_pro\AnomalyDetectionCVPR2018-Pytorch\web_demo\static\\videos\\Arrest.mp4",
    "Arson": r"S:\\ano_dec_pro\AnomalyDetectionCVPR2018-Pytorch\web_demo\static\\videos\\Arson.mp4",
    "Assault": r"S:\\ano_dec_pro\AnomalyDetectionCVPR2018-Pytorch\web_demo\static\\videos\\Assault.mp4",
    "Burglary": r"S:\\ano_dec_pro\AnomalyDetectionCVPR2018-Pytorch\web_demo\static\\videos\\Burglary.mp4",
    "Explosion": r"S:\\ano_dec_pro\AnomalyDetectionCVPR2018-Pytorch\web_demo\static\\videos\\Explosion.mp4",
    "Fighting": r"S:\\ano_dec_pro\AnomalyDetectionCVPR2018-Pytorch\web_demo\static\\videos\\Fighting.mp4",
    "RoadAccidents": r"S:\\ano_dec_pro\AnomalyDetectionCVPR2018-Pytorch\web_demo\static\\videos\\RoadAccidents.mp4",
    "Robbery": r"S:\\ano_dec_pro\AnomalyDetectionCVPR2018-Pytorch\web_demo\static\\videos\\Robbery.mp4",
    "Shooting": r"S:\\ano_dec_pro\AnomalyDetectionCVPR2018-Pytorch\web_demo\static\\videos\\Shooting.mp4",
    "Shoplifting": r"S:\\ano_dec_pro\AnomalyDetectionCVPR2018-Pytorch\web_demo\static\\videos\\Shoplifting.mp4",
    "Stealing": r"S:\\ano_dec_pro\AnomalyDetectionCVPR2018-Pytorch\web_demo\static\\videos\\Stealing.mp4",
    "Vandalism": r"S:\\ano_dec_pro\AnomalyDetectionCVPR2018-Pytorch\web_demo\static\\videos\\Vandalism.mp4",
    "Normal": r"S:\\ano_dec_pro\AnomalyDetectionCVPR2018-Pytorch\web_demo\static\\videos\\Normal.mp4"
}

# --- Threading control ---
thread = None
thread_lock = threading.Lock()
stop_event = threading.Event()

# (The `smooth_score` and `video_processing_task` functions remain unchanged from the previous version)
def smooth_score(scores, new_score, window=5):
    scores.append(new_score)
    if len(scores) > window:
        scores.pop(0)
    return float(np.mean(scores))

def video_processing_task(video_path):
    global thread
    try:
        cap = cv2.VideoCapture(video_path)
        if not cap.isOpened():
            socketio.emit('processing_error', {'error': f'Could not open video file.'})
            return
        frame_buffer = []
        last_save_time = 0
        recent_scores = []
        FRAME_SKIP = 4 
        frame_count = 0
        while cap.isOpened() and not stop_event.is_set():
            socketio.sleep(0.001)
            ret, frame = cap.read()
            if not ret: break
            frame_count += 1
            if frame_count % (FRAME_SKIP + 1) != 0: continue
            frame_buffer.append(frame.copy())
            if len(frame_buffer) == 16:
                frames_resized = [cv2.resize(f, (112, 112)) for f in frame_buffer]
                clip_np = np.array(frames_resized, dtype=np.uint8)
                clip_torch = torch.from_numpy(clip_np)
                clip_torch = TRANSFORMS(clip_torch)
                clip_torch = clip_torch.unsqueeze(0).to(DEVICE)
                with torch.no_grad():
                    features = feature_extractor(clip_torch).detach()
                    score_tensor = anomaly_detector(features).detach()
                    score = float(score_tensor.view(-1)[0].item())
                score = smooth_score(recent_scores, score)
                score = float(np.clip(score, 0, 1))
                socketio.emit('update_graph', {'score': score})
                if score > ANOMALY_THRESHOLD and (time.time() - last_save_time) >= 30:
                    last_save_time = time.time()
                    socketio.emit('update_status', {'status': 'Anomaly detected! Saving clip...'})
                    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
                    clip_dir = os.path.join(SAVE_DIR, f"anomaly_{timestamp}")
                    os.makedirs(clip_dir, exist_ok=True)
                    first_frame_path = os.path.join(clip_dir, "anomaly_frame.jpg")
                    cv2.imwrite(first_frame_path, frame_buffer[0])
                    try:
                        yolo_result = analyze_video_with_yolo(first_frame_path, model_path=YOLO_MODEL_PATH, return_class=True)
                        socketio.emit('update_yolo_text', {'text': f"YOLO Class: {yolo_result}"})
                        _, buffer = cv2.imencode('.jpg', frame_buffer[0])
                        b64_str = base64.b64encode(buffer).decode('utf-8')
                        socketio.emit('update_yolo_image', {'image_data': b64_str})
                    except Exception as e:
                        socketio.emit('update_yolo_text', {'text': f'YOLO Error: {e}'})
                frame_buffer.clear()
        cap.release()
        if not stop_event.is_set():
            socketio.emit('processing_finished', {'message': 'Video finished.'})
    finally:
        with thread_lock:
            thread = None
            stop_event.clear()

@app.route('/')
def index():
    return render_template('index.html', anomaly_names=VIDEO_PATHS.keys())

# ADDED: New route for handling video uploads
@app.route('/upload', methods=['POST'])
def upload_file():
    if 'video' not in request.files:
        return jsonify({'error': 'No video file found'}), 400
    file = request.files['video']
    if file.filename == '':
        return jsonify({'error': 'No video file selected'}), 400
    if file:
        filename = secure_filename(file.filename)
        # Add a timestamp to make filenames unique
        unique_filename = f"{datetime.now().strftime('%Y%m%d%HM%S')}_{filename}"
        save_path = os.path.join(app.config['UPLOAD_FOLDER'], unique_filename)
        file.save(save_path)
        return jsonify({'success': True, 'filename': unique_filename})
    return jsonify({'error': 'File upload failed'}), 500

# MODIFIED: This route now streams both demo and uploaded videos
@app.route('/video_stream/<source>/<filename>')
def video_stream(source, filename):
    if source == 'demo':
        path = VIDEO_PATHS.get(filename)
    elif source == 'upload':
        path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
    else:
        return "Invalid source", 404

    if not path or not os.path.exists(path):
        return "Video not found", 404
    
    def generate():
        with open(path, "rb") as f:
            while chunk := f.read(1024 * 1024):
                yield chunk
                
    return Response(generate(), mimetype="video/mp4")

@socketio.on('start_processing')
def handle_start_processing(data):
    global thread
    with thread_lock:
        if thread is None:
            stop_event.clear()
            source = data.get('source')
            filename = data.get('filename')
            video_path = None

            if source == 'demo':
                video_path = VIDEO_PATHS.get(filename)
            elif source == 'upload':
                video_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)

            if video_path and os.path.exists(video_path):
                print(f"[INFO] Starting processing for {filename} from {source}")
                thread = socketio.start_background_task(target=video_processing_task, video_path=video_path)
            else:
                socketio.emit('processing_error', {'error': f'Video file not found!'})

@socketio.on('reset_system')
def handle_reset():
    global thread
    with thread_lock:
        if thread is not None:
            stop_event.set()
    socketio.emit('system_reset_confirm')

if __name__ == '__main__':
    print("[INFO] Starting Flask server...")
    socketio.run(app, debug=True)